# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Binary to run train and evaluation on object detection model."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import flags

import tensorflow as tf

from object_detection import model_hparams
from object_detection import model_lib

tf.logging.set_verbosity(tf.logging.INFO)


flags.DEFINE_string(
    "model_dir",
    None,
    "Path to output model directory "
    "where event and checkpoint files will be written.",
)
flags.DEFINE_string("pipeline_config_path", None, "Path to pipeline config " "file.")
flags.DEFINE_integer("num_train_steps", None, "Number of train steps.")
flags.DEFINE_boolean(
    "eval_training_data",
    False,
    "If training data should be evaluated for this job. Note "
    "that one call only use this in eval-only mode, and "
    "`checkpoint_dir` must be supplied.",
)
flags.DEFINE_integer(
    "sample_1_of_n_eval_examples",
    1,
    "Will sample one of " "every n eval input examples, where n is provided.",
)
flags.DEFINE_integer(
    "sample_1_of_n_eval_on_train_examples",
    5,
    "Will sample "
    "one of every n train input examples for evaluation, "
    "where n is provided. This is only used if "
    "`eval_training_data` is True.",
)
flags.DEFINE_integer(
    "log_step_count_steps",
    100,
    "The frequency, in number "
    "of global steps, that the global step and the loss will "
    "be logged during training. Also controls the frequency "
    "that the global steps / s will be logged (and written to "
    "summary) during training.",
)
flags.DEFINE_string(
    "hparams_overrides",
    None,
    "Hyperparameter overrides, "
    "represented as a string containing comma-separated "
    "hparam_name=value pairs.",
)
flags.DEFINE_string(
    "checkpoint_dir",
    None,
    "Path to directory holding a checkpoint.  If "
    "`checkpoint_dir` is provided, this binary operates in eval-only mode, "
    "writing resulting metrics to `model_dir`.",
)
flags.DEFINE_boolean(
    "run_once",
    False,
    "If running in eval-only mode, whether to run just "
    "one round of eval vs running continuously (default).",
)
FLAGS = flags.FLAGS


def main(unused_argv):
    flags.mark_flag_as_required("model_dir")
    flags.mark_flag_as_required("pipeline_config_path")
    config = tf.estimator.RunConfig(
        model_dir=FLAGS.model_dir, log_step_count_steps=FLAGS.log_step_count_steps
    )

    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config=config,
        hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
        pipeline_config_path=FLAGS.pipeline_config_path,
        train_steps=FLAGS.num_train_steps,
        sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
        sample_1_of_n_eval_on_train_examples=(
            FLAGS.sample_1_of_n_eval_on_train_examples
        ),
    )
    estimator = train_and_eval_dict["estimator"]
    train_input_fn = train_and_eval_dict["train_input_fn"]
    eval_input_fns = train_and_eval_dict["eval_input_fns"]
    eval_on_train_input_fn = train_and_eval_dict["eval_on_train_input_fn"]
    predict_input_fn = train_and_eval_dict["predict_input_fn"]
    train_steps = train_and_eval_dict["train_steps"]

    if FLAGS.checkpoint_dir:
        if FLAGS.eval_training_data:
            name = "training_data"
            input_fn = eval_on_train_input_fn
        else:
            name = "validation_data"
            # The first eval input will be evaluated.
            input_fn = eval_input_fns[0]
        if FLAGS.run_once:
            estimator.evaluate(
                input_fn,
                steps=None,
                checkpoint_path=tf.train.latest_checkpoint(FLAGS.checkpoint_dir),
            )
        else:
            model_lib.continuous_eval(
                estimator, FLAGS.checkpoint_dir, input_fn, train_steps, name
            )
    else:
        train_spec, eval_specs = model_lib.create_train_and_eval_specs(
            train_input_fn,
            eval_input_fns,
            eval_on_train_input_fn,
            predict_input_fn,
            train_steps,
            eval_on_train_data=False,
        )

        # Currently only a single Eval Spec is allowed.
        tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])


if __name__ == "__main__":
    tf.app.run()
